Word Error Rate Estimation for Speech Recognition: e-WER

Ahmed Ali, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we propose a novel approach to estimate WER, or e-WER, which does not require a gold-standard transcription of the test set. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. We report results for the two features; black-box and glass-box using unseen 24 Arabic broadcast programs. Our system achieves 16.9% WER root mean squared error (RMSE) across 1,400 sentences. The estimated overall WER e-WER was 25.3% for the three hours test set, while the actual WER was 28.5%.
Original languageEnglish
Title of host publicationProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Place of PublicationMelbourne, Australia
PublisherAssociation for Computational Linguistics (ACL)
Number of pages5
Publication statusPublished - Jul 2018


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